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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Using machine learning to examine the relationship between asthma and absenteeism.

Maria-Anna Lary1, Leslie Allsopp1, David J Lary2

  • 1Department of Biostatistics and Epidemiology, School of Public Health, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA.

Environmental Monitoring and Assessment
|June 30, 2019
PubMed
Summary

Machine learning accurately predicted student learning outcomes by analyzing factors like school absences. Childhood asthma emerged as a significant predictor of absences, underscoring the link between environmental public health and academic success.

Keywords:
AbsenteeismAsthmaEnvironmental & Public HealthLearning outcomesMachine learning

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Area of Science:

  • Educational Data Mining
  • Environmental Health
  • Machine Learning Applications

Background:

  • Student learning outcomes are influenced by various factors, including attendance.
  • Geospatial analysis can reveal patterns in educational data.
  • Environmental factors may impact student health and, consequently, their academic performance.

Purpose of the Study:

  • To assess the efficacy of machine learning in estimating student learning outcomes across a large school district.
  • To identify key predictors of student learning outcomes and school absences.
  • To explore the relationship between environmental public health and educational attainment.

Main Methods:

  • Utilized machine learning algorithms to analyze student data, including attendance and health information.
  • Employed geospatial techniques to map learning outcomes across school campuses.
  • Correlated student absences with health conditions, specifically asthma.

Main Results:

  • Machine learning models effectively estimated student learning outcomes with geospatial accuracy.
  • The number of student absences was a primary factor in predicting learning outcomes.
  • Childhood asthma was identified as a significant predictor of student absences.

Conclusions:

  • Machine learning provides a powerful tool for understanding and predicting student learning outcomes.
  • Student attendance is a critical determinant of academic success.
  • Environmental public health, particularly the prevalence of conditions like asthma, plays a crucial role in student attendance and learning.